Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Partial parsing via finite-state cascades
Natural Language Engineering
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Exploring evidence for shallow parsing
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Probabilistic context-free grammar induction based on structural zeros
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Linear complexity context-free parsing pipelines via chart constraints
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Simulating morphological analyzers with stochastic taggers for confidence estimation
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Simple unsupervised grammar induction from raw text with cascaded finite state models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Finite-state chart constraints for reduced complexity context-free parsing pipelines
Computational Linguistics
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In this paper, we look at comparing high-accuracy context-free parsers with high-accuracy finite-state (shallow) parsers on several shallow parsing tasks. We show that previously reported comparisons greatly under-estimated the performance of context-free parsers for these tasks. We also demonstrate that context-free parsers can train effectively on relatively little training data, and are more robust to domain shift for shallow parsing tasks than has been previously reported. Finally, we establish that combining the output of context-free and finite-state parsers gives much higher results than the previous-best published results, on several common tasks. While the efficiency benefit of finite-state models is inarguable, the results presented here show that the corresponding cost in accuracy is higher than previously thought.